Related Pages

Related topics: Persistence, Checkpointing & Long-Term Memory, Streaming Protocols, Prebuilt Patterns & Multi-Agent Ecosystem

Core Framework: StateGraph, Channels & Pregel Runtime

Purpose and Scope

The Core Framework — primarily the libs/langgraph-core package and the public langgraph entry point — provides the runtime that turns a developer-defined graph of nodes and edges into a stateful, multi-actor application. Its stated mission is to "build stateful, multi-actor applications with LLMs" (libs/langgraph/package.json).

The framework is explicitly inspired by Pregel, Apache Beam, and NetworkX, as noted in the project README. Pregel and Beam supply the *superstep* and *channel* execution model, while NetworkX shapes the public graph-construction interface (README.md).

The framework is split into three cooperating layers, each living in libs/langgraph-core/src:

Source: libs/langgraph-core/src/graph/state.ts, libs/langgraph-core/src/channels/index.ts, libs/langgraph-core/src/pregel/algo.ts.

StateGraph and Annotation

A StateGraph is the developer-facing API used to assemble a graph: you declare a state schema via Annotation, add nodes (functions that read and return partial state), and connect them with conditional or static edges. The create-langgraph CLI scans for these patterns to auto-generate a langgraph.json deployment file:

// ESM exports that the scanner recognises
export const agent = createAgent({ model, tools });
export const graph = new StateGraph(annotation).compile();
export const app = workflow.compile();

Source: libs/create-langgraph/README.md.

The annotation module is what gives each state key its reducer — the function that merges the partial update produced by a node into the existing channel value. The messages_reducer module is the canonical reducer for the messages key, handling append, replace, and ID-based updates of chat messages (libs/langgraph-core/src/graph/messages_reducer.ts).

A subtle but important constraint surfaces in community issue #1722: LangGraph Studio's Chat tab does not appear when a graph is built inside a class. Because the create-langgraph scanner only matches *exported* call sites of createAgent, new StateGraph(...).compile(), and workflow.compile(), encapsulation inside a class hides the graph from the tooling — a useful reminder that graph construction is a top-level pattern in this framework.

Channels

Channels are the per-key storage primitives the runtime reads from and writes to each superstep. They are the bridge between the developer-friendly StateGraph model and the Pregel-style execution engine. The channel layer exposes:

• BaseChannel — abstract channel with update, get, consume, and checkpoint operations.
• Reducer-backed channels (e.g. BinaryOperatorAggregate, LastValue) — apply a reducer function to merge a node's partial return into the current value.
• Topic and named-barrier channels — used for fan-in/fan-out patterns such as Send / Map-Reduce.

Source: libs/langgraph-core/src/channels/index.ts.

Channels also implement the checkpoint interface consumed by @langchain/langgraph-checkpoint, the companion package that defines "base interfaces for LangGraph checkpoint savers" (libs/checkpoint/package.json). This is how the runtime persists channel state to backends like an in-memory MemorySaver or a Postgres store.

The libs/checkpoint/src/store/base.ts file shows the lower-level storage operations (PutOperation with namespace, key, value) that a durable channel writer uses, demonstrating that channels are not just in-memory buffers — they can be backed by a long-term store for human-in-the-loop and time-travel debugging (libs/checkpoint/src/store/base.ts).

Pregel Runtime

The Pregel runtime (libs/langgraph-core/src/pregel/) executes the compiled graph. It runs the canonical superstep loop:

flowchart LR
    A[Plan superstep<br/>from pending nodes] --> B[Read channel values<br/>into node input]
    B --> C[Invoke nodes<br/>in parallel]
    C --> D[Apply channel<br/>reducers / writes]
    D --> E{More<br/>steps?}
    E -- yes --> A
    E -- no --> F[Emit final<br/>output]

The algo.ts module houses the superstep planner, the conditional-edge resolver, and the integration with channels. By staying faithful to Pregel's BSP (Bulk Synchronous Parallel) model, the runtime preserves the determinism and replayability guarantees that checkpointing depends on.

The framework's description in the package metadata underscores that this runtime is the foundation of features higher-level libraries build on: "out-of-box support for streaming, short-term and long-term memory and human-in-the-loop" (libs/langgraph-supervisor/README.md, libs/langgraph-swarm/README.md). These higher-level libraries (supervisor, swarm, CUA) compile their multi-agent topologies into the same StateGraph + Pregel primitives, which is why they can be deployed uniformly through the CLI scanner.

Community issue #107 notes that the Python version supports PNG visualization of a StateGraph; in the JS port, the same graph metadata is introspectable but no first-party visualizer ships with the runtime.

Configuration and Common Pitfalls

The core framework ships as dual ESM/CJS with a strict Node engine floor:

Source: libs/langgraph/package.json, libs/checkpoint/package.json.

Two recurring community failure modes are worth flagging when configuring this runtime:

1. AsyncLocalStorage in the browser — issue #1699 reports that async_hooks cannot be used in the browser under @langchain/core 1.0.0-alpha. The Pregel runtime relies on AsyncLocalStorage for context propagation per run, so browser-only deployments must remain on the stable core line.
2. Streaming through a proxy — issue #1295 describes useStream failing to update messages when a Next.js proxy sits in front of a LangGraph server. The root cause lies in how SSE events emitted by the runtime are re-encoded by the proxy; the framework itself streams correctly when consumed directly.

See Also

• Checkpoint and Long-Term Memory
• LangGraph SDK and Streaming
• Multi-Agent Supervisor
• Swarm Multi-Agent
• Computer Use Agent

Related Pages

Related topics: Core Framework: StateGraph, Channels & Pregel Runtime, SDK, API Server, CLI & Frontend Framework Integrations

Persistence, Checkpointing & Long-Term Memory

Overview and Core Concepts

LangGraph.js provides two complementary persistence abstractions that together power durable, stateful agents. The first is checkpointing, which captures the short-term, per-thread state of a graph run at every super-step and lets an execution be paused, replayed, resumed, or branched ("forked") after a failure. The second is a store, which provides long-term, cross-thread memory keyed by a hierarchical namespace. Both are wired into a graph at compile time via checkpointer and store arguments to compile() (Source: libs/langgraph-supervisor/README.md:18-30).

Checkpointing is what enables the headline features described in the project README: durable execution, human-in-the-loop (interrupt), and comprehensive memory (Source: libs/langgraph/README.md:9-19). A thread represents one ongoing conversation or run; a checkpoint is one snapshot of that thread's state; pending writes are intermediate channel mutations not yet committed to a checkpoint.

Checkpoint Saver Architecture

The core contract is the abstract BaseCheckpointSaver<V> class in libs/checkpoint/src/base.ts. Every concrete saver must implement getTuple, list, put, and putWrites, and may override deleteThread (Source: libs/checkpoint/src/base.ts:60-110). The data model is intentionally minimal so that multiple backends can be supported:

A CheckpointMetadata distinguishes how a checkpoint was created: input (initial invoke/stream), loop (created inside the Pregel loop), update (manual state update), or fork (copy of another) (Source: libs/checkpoint/src/types.ts:60-95). The step is -1 for the first input checkpoint, 0 for the first loop checkpoint, and increments from there.

The base class also defines a serde: SerializerProtocol slot — by default JsonPlusSerializer — that any concrete saver can swap to control how channel values are encoded (Source: libs/checkpoint/src/base.ts:64-67).

flowchart LR
    A[Graph Node] -->|PendingWrite| B[BaseCheckpointSaver]
    B -->|putWrites| C[(Backend Storage)]
    B -->|put / getTuple| C
    C -->|list| D[Pregel Loop]
    D -->|fork / resume| A
    H[compile store] --> S[BaseStore]
    S -->|batch: put / get / search| C

Concrete implementations live in dedicated packages. MemorySaver (in-process, for development) and PostgresSaver share the contract above, with PostgresSaver using pg.Pool and configurable schema (default public) (Source: libs/checkpoint-postgres/src/index.ts:18-45). Schema management is delegated to getMigrations, which emits DDL for the checkpoint_* and checkpoint_writes tables and is applied automatically (Source: libs/checkpoint-postgres/src/migrations.ts:1-25). For Redis, the RedisSaver and memory-optimized ShallowRedisSaver ship in @langchain/langgraph-checkpoint-redis; both require RedisJSON and RediSearch modules (bundled in Redis 8.0+, otherwise via Redis Stack) (Source: libs/checkpoint-redis/README.md:13-29).

Long-Term Memory Stores

Where a checkpointer scopes its keys to a single thread_id, a store keys items by a namespace: string[] plus a key: string, giving a folder-like path that spans users, sessions, and applications (Source: libs/checkpoint/src/store/base.ts:25-55). The PutOperation and SearchOperation interfaces cover the CRUD plus query surface; setting value: null in a PutOperation signals a delete (Source: libs/checkpoint/src/store/base.ts:60-95).

InMemoryStore is the reference implementation. It indexes items lazily based on an optional IndexConfig, tokenizing JSON paths (e.g. metadata.title) and supporting vector similarity search alongside lexical query strings (Source: libs/checkpoint/src/store/memory.ts:25-55). A RedisStore counterpart is shipped by the Redis package. The supervisor pattern shows the typical wiring: a MemorySaver for per-thread checkpoints plus an InMemoryStore for cross-agent memory, both passed to compile() (Source: libs/langgraph-supervisor/README.md:18-30).

Custom Savers and Validation

Teams that need a custom backend (e.g., DynamoDB, Spanner) can extend BaseCheckpointSaver and validate it with @langchain/langgraph-checkpoint-validation. The validation tool requires Node 20, 22, or 24, and runs against a user-supplied CheckpointerTestInitializer that knows how to provision and clean up a checkpointer instance (Source: libs/checkpoint-validation/README.md:9-15). Initializer functions may be sync or async; the harness awaits them as needed.

Operational Notes and Community Context

• Engines: @langchain/langgraph-checkpoint declares node >= 18 (Source: libs/checkpoint/package.json:6-9). Browser users have reported that async_hooks (used by some checkpoint paths) cannot run in the browser at @langchain/core ^1.0.0-alpha.x (issue #1699); production deployments therefore typically target a Node server.
• Studio integration: When a graph is built inside a class, LangGraph Studio may fail to surface the Chat tab (issue #1722). Keeping the compiled graph reachable as a top-level export, alongside the configured checkpointer, is the recommended workaround.
• Streaming proxies: The React useStream hook can fail to populate state when wrapped behind a Next.js proxy that strips SSE framing (issue #1295); this is a transport concern rather than a checkpointing one, but it affects how persisted state is surfaced to UIs.
• Delta channel history: BaseCheckpointSaver.getDeltaChannelHistory exposes a per-channel view of writes plus the nearest ancestor seed, marked as beta in the source (Source: libs/checkpoint/src/types.ts:10-30). Field names and semantics may change.

See Also

• Durable execution
• Human-in-the-loop / interrupts
• Memory concepts
• LangSmith deployments
• API Reference: langchain-langgraph

Related Pages

Related topics: Core Framework: StateGraph, Channels & Pregel Runtime, Persistence, Checkpointing & Long-Term Memory, Streaming Protocols, Prebuilt Patterns & Multi-Agent Ecosystem

SDK, API Server, CLI & Frontend Framework Integrations

The langgraphjs monorepo ships a complete stack for moving from a locally defined LangGraph to a deployable agent service. The pieces are layered: a server-runtime graph library (libs/langgraph), a stateful checkpoint + store layer (libs/checkpoint/src/store/base.ts), a thin server-side API client (libs/sdk), React/Vue framework bindings (libs/sdk-react), and a project scaffolder CLI (libs/create-langgraph). Together they let developers define a graph in TypeScript, expose it over HTTP, stream its execution to a UI, and persist long-term memory.

LangGraph JS/TS SDK

The @langchain/langgraph-sdk package is the canonical client for the LangGraph API server. It exposes a single Client class that, by default, points at http://localhost:2024 (the URL of langgraph dev) when no apiUrl is provided (libs/sdk/README.md). Five sub-clients hang off the root client, each wrapping a single API surface:

The recommended entry point for streaming is client.threads.stream({ assistantId: "my-agent" }), which returns a ThreadStream with typed, lazy projections: thread.messages, thread.toolCalls, thread.values, thread.output, thread.interrupts, thread.subgraphs, thread.subagents, plus media iterators (thread.audio, thread.images, thread.video, thread.files) and a custom thread.extensions.<name> channel (libs/sdk/README.md). The store sub-client operates on the data shape defined in libs/checkpoint/src/store/base.ts, where a PutOperation is described by a hierarchical namespace (e.g. ["documents", "user123"]), a key, and a JSON-serializable value (or null to delete).

React & Vue Frontend Integrations

The @langchain/react package (v1) is a v2-native React SDK that ships a useStream hook for live UI updates. Always-on root projections (values, messages, toolCalls, interrupts) cost zero per-subscription, while less common namespaces — subagent messages, media streams, submission queue, message metadata, raw channels — are opt-in via small companion selector hooks that open ref-counted subscriptions and release them on unmount (libs/sdk-react/README.md). The transport is session-based and automatically re-attaches on remount, eliminating older flows like reconnectOnMount or joinStream. useStream<typeof agent>() infers state, tool calls, and subagent state maps from the agent brand, and the hosted-server path and custom-adapter path form a single discriminated union so they cannot be mixed at compile time.

A parallel Vue adapter exists; the latest published release is @langchain/[email protected], which tracks the @langchain/langgraph-sdk family (community release notes).

flowchart LR
    UI[React/Vue UI] -->|useStream| Hook[Selector Hooks]
    Hook -->|SSE| API[LangGraph API Server]
    API -->|invoke| Graph[Compiled StateGraph]
    Graph -->|read/write| Store[(Checkpoint + Store)]
    Graph -->|tokens| API
    API -->|events| Hook

Known integration caveats

• #1295 — useStream through a Next.js proxy. When useStream is consumed behind a Next.js API proxy, the hook may receive and parse SSE events correctly but still fail to populate messages or values. This is an open regression observed against @langchain/[email protected] and is the most active frontend-streaming bug in the issue tracker (issue #1295).
• #1699 — async_hooks in the browser. Versions of @langchain/core in the ^1.0.0-alpha.x range cannot use async_hooks in a browser environment, which surfaces in proxy/middleware paths used by the SDK (issue #1699).
• #504 — Vercel data-stream protocol. A long-standing request asks LangGraph to natively speak Vercel's AI-SDK data-stream protocol so React apps already using the AI SDK client can adopt LangGraph as a drop-in (issue #504).
• #1722 — Studio Chat tab inside a class. LangGraph Studio fails to surface the Chat tab when the graph is constructed inside a class because the inspector looks for module-scope agent exports (issue #1722).

Project Scaffolding CLI

create-langgraph is the official CLI for bootstrapping a project and for reverse-engineering a langgraph.json config from an existing codebase (libs/create-langgraph/README.md). It exposes two subcommands:

• create-langgraph — downloads a selected template from GitHub, extracts it to a target directory, optionally initializes Git, and prints next steps.
• create-langgraph config [path] — scans a project for LangGraph agents and writes a langgraph.json configuration. Detection covers createAgent({ model, tools }), new StateGraph(...).compile(), and the workflow-builder pattern workflow.compile() / builder.compile() across both ESM (export const …) and CommonJS (module.exports / exports.…) styles. Only exported agents are included; unexported ones are listed as warnings, which directly addresses issue #1722's underlying detection rule.

The CLI is built on @clack/prompts and commander, ships as an ESM package, and is published under [email protected] (libs/create-langgraph/package.json).

See Also

• Graph runtime and streaming core
• Checkpoint & Store API
• Computer-Use Agent (@langchain/langgraph-cua)
• React SDK deep dive
• Vercel AI-SDK interop discussion (#504)

Related Pages

Related topics: Core Framework: StateGraph, Channels & Pregel Runtime, SDK, API Server, CLI & Frontend Framework Integrations

Streaming Protocols, Prebuilt Patterns & Multi-Agent Ecosystem

Overview

LangGraph.js is a JavaScript/TypeScript framework for building stateful, long-running agent applications. The repository is organised as a monorepo that ships a core graph runtime, a streaming-capable HTTP/WebSocket SDK, framework adapters (React, Vue, Angular, Svelte), and a family of prebuilt multi-agent libraries (supervisor, swarm, computer-use-agent). Source: README.md and libs/sdk/README.md.

Streaming Protocols

V2-Native Streaming Protocol

The current SDK family is built around a v2 streaming protocol that is session-based and supports automatic re-attach on remount. The protocol supersedes the older reconnectOnMount / joinStream choreography. Source: libs/sdk-react/README.md ("v2-native streaming protocol. Session-based transport with automatic re-attach on remount; no more reconnectOnMount / joinStream dance.")

The transport is discriminated between a hosted Agent Server path and a custom-adapter path; mixing them is a compile-time error. The root useStream / injectStream / useStream (Svelte) handle exposes always-on projections for values, messages, toolCalls, and interrupts, with namespaced data (subagents, subgraphs, media, submission queue, per-message metadata) delivered through ref-counted selector hooks so components only pay for data they consume. Source: libs/sdk-react/README.md.

Framework Adapters and Stream Channels

Each framework adapter normalises the protocol to its native reactivity primitive:

For Angular, the returned handle is signal-based: stream.toolCalls() (a Signal<InferToolCalls<T>[]>), stream.interrupts() (a Signal<Interrupt<InterruptType>[]>), and stream.isLoading() (a Signal<boolean>) are driven by root-namespace lifecycle events. Source: libs/sdk-angular/src/use-stream.ts. Svelte exposes the same fields as getters on a stable stream handle so templates and $derived expressions track updates automatically — destructuring freezes the value, so callers must use stream.messages rather than const { messages } = stream. Source: libs/sdk-svelte/README.md.

Custom Transports and Browser Considerations

For non-default backends the SDK exposes an AgentServerAdapter interface, with an HttpAgentServerAdapter reference implementation, that can target SSE, WebSocket, or a custom HTTP boundary. Source: libs/sdk-react/README.md and libs/sdk-svelte/README.md. Community issue #1295 documents a known edge case where useStream fails to populate messages / values when the agent server is reached through a Next.js API proxy, even though the underlying SSE events arrive correctly. Community issue #1699 tracks browser-side incompatibility between @langchain/core 1.0.0-alpha and Node's async_hooks. These are environment-level caveats rather than protocol defects.

Community issue #504 (Vercel AI SDK data-stream protocol) highlights ongoing interest in adopting the Vercel data-stream wire format for drop-in compatibility with the AI SDK client. The v2 protocol's discriminated transport design is the foundation that would make such an adapter possible; today clients must continue using the SDK's own projections.

Prebuilt Multi-Agent Patterns

Supervisor and Swarm

Two first-party libraries implement the most common multi-agent coordination styles. The supervisor library creates a hierarchical system in which a central supervisor agent decides which specialised agent to invoke and mediates all message flow via tool-based handoffs. Source: libs/langgraph-supervisor/README.md. The swarm library offers a peer-to-peer alternative: agents hand off to each other through createHandoffTool() and createSwarm(), retaining short-term and long-term memory plus human-in-the-loop support from the underlying LangGraph runtime. Source: libs/langgraph-swarm/README.md.

flowchart LR
    User --> Sup[Supervisor Agent]
    Sup -- tool handoff --> A1[Specialist A]
    Sup -- tool handoff --> A2[Specialist B]
    A1 -. result .-> Sup
    A2 -. result .-> Sup
    Sup --> User

Computer Use Agent (CUA)

@langchain/langgraph-cua provides a ready-made computer-use agent built on LangGraph. It uses Scrapybara to drive a virtual machine, OpenAI as the policy model, and ships with a typed createCua() factory plus peer-deps on @langchain/langgraph, @langchain/core, and @langchain/openai. Source: libs/langgraph-cua/README.md and libs/langgraph-cua/package.json.

Streaming Example Set

The examples/streaming workspace enumerates concrete recipes for both in-process and remote execution paths: basic, messages (including a model mode for token-level streaming), parallel, custom-transformer, subgraphs, human-in-the-loop, subagents, subagent-status, and a2a (agent-to-agent). Source: examples/streaming/package.json.

Project Scaffolding and Visualisation

The create-langgraph CLI bootstraps new agents from a template registry and ships a config subcommand that scans a project for createAgent(), new StateGraph(...).compile(), and workflow.compile() patterns, generating a langgraph.json for the LangGraph Platform. Only exported agents are included; unexported ones surface as warnings. Source: libs/create-langgraph/README.md. Community issue #107 (PNG visualisation parity with the Python port) and issue #1722 (LangGraph Studio failing to show the Chat tab when a graph is defined inside a class) remain open and reflect the trade-off between dynamic graph construction and Studio's static introspection. Source: libs/create-langgraph/README.md.

See Also

• LangGraph Core Concepts & State Management
• React / Vue / Angular / Svelte SDK Reference
• Checkpointing & Long-Term Memory Store
• LangGraph Platform & langgraph.json Configuration

Doramagic Pitfall Log

Found 11 structured pitfall item(s), including 0 high/blocking item(s). Top priority: Identity risk - Identity risk requires verification.

1. Identity risk: Identity risk requires verification

• Severity: medium
• Finding: Project evidence flags a identity risk. Review the linked source before relying on this workflow.
• User impact: May increase setup, validation, or first-run risk for the user.
• Recommended check: Reproduce the official install and quickstart path in an isolated environment.
• Evidence: identity.distribution | https://www.npmjs.com/package/@langchain/langgraph

2. Installation risk: Installation risk requires verification

• Severity: medium
• Finding: Project evidence flags a installation risk. Review the linked source before relying on this workflow.
• User impact: May increase setup, validation, or first-run risk for the user.
• Recommended check: Reproduce the official install and quickstart path in an isolated environment.
• Evidence: community_evidence:github | https://github.com/langchain-ai/langgraphjs/issues/2496

3. Capability evidence risk: Capability evidence risk requires verification

• Severity: medium
• Finding: README/documentation is current enough for a first validation pass.
• User impact: May increase setup, validation, or first-run risk for the user.
• Recommended check: Reproduce the official install and quickstart path in an isolated environment.
• Evidence: capability.assumptions | https://www.npmjs.com/package/@langchain/langgraph

4. Runtime risk: Runtime risk requires verification

• Severity: medium
• Finding: Project evidence flags a runtime risk. Review the linked source before relying on this workflow.
• User impact: May increase setup, validation, or first-run risk for the user.
• Recommended check: Reproduce the official install and quickstart path in an isolated environment.
• Evidence: community_evidence:github | https://github.com/langchain-ai/langgraphjs/issues/2551

5. Maintenance risk: Maintenance risk requires verification

• Severity: medium
• Finding: Project evidence flags a maintenance risk. Review the linked source before relying on this workflow.
• User impact: May increase setup, validation, or first-run risk for the user.
• Recommended check: Reproduce the official install and quickstart path in an isolated environment.
• Evidence: evidence.maintainer_signals | https://www.npmjs.com/package/@langchain/langgraph

6. Security or permission risk: Security or permission risk requires verification

• Severity: medium
• Finding: no_demo
• User impact: May increase setup, validation, or first-run risk for the user.
• Recommended check: Reproduce the official install and quickstart path in an isolated environment.
• Evidence: downstream_validation.risk_items | https://www.npmjs.com/package/@langchain/langgraph

7. Security or permission risk: Security or permission risk requires verification

• Severity: medium
• Finding: no_demo
• User impact: May increase setup, validation, or first-run risk for the user.
• Recommended check: Reproduce the official install and quickstart path in an isolated environment.
• Evidence: risks.scoring_risks | https://www.npmjs.com/package/@langchain/langgraph

8. Security or permission risk: Security or permission risk requires verification

• Severity: medium
• Finding: Project evidence flags a security or permission risk. Review the linked source before relying on this workflow.
• User impact: May increase setup, validation, or first-run risk for the user.
• Recommended check: Reproduce the official install and quickstart path in an isolated environment.
• Evidence: community_evidence:github | https://github.com/langchain-ai/langgraphjs/issues/2040

9. Security or permission risk: Security or permission risk requires verification

• Severity: medium
• Finding: Project evidence flags a security or permission risk. Review the linked source before relying on this workflow.
• User impact: May increase setup, validation, or first-run risk for the user.
• Recommended check: Reproduce the official install and quickstart path in an isolated environment.
• Evidence: community_evidence:github | https://github.com/langchain-ai/langgraphjs/issues/1836

10. Maintenance risk: Maintenance risk requires verification

• Severity: low
• Finding: issue_or_pr_quality=unknown。
• User impact: May increase setup, validation, or first-run risk for the user.
• Recommended check: Reproduce the official install and quickstart path in an isolated environment.
• Evidence: evidence.maintainer_signals | https://www.npmjs.com/package/@langchain/langgraph

11. Maintenance risk: Maintenance risk requires verification

• Severity: low
• Finding: release_recency=unknown。
• User impact: May increase setup, validation, or first-run risk for the user.
• Recommended check: Reproduce the official install and quickstart path in an isolated environment.
• Evidence: evidence.maintainer_signals | https://www.npmjs.com/package/@langchain/langgraph

Community Discussion Evidence

Doramagic exposes project-level community discussion separately from official documentation. Review these links before using langgraphjs with real data or production workflows.

• Cross-thread checkpoint data contamination when using singleton agent wi - github / github_issue
• What format data does AIMessage has to have to give contentBlock wit - github / github_issue
• streamEvents v3 produces AIMessage with empty tool_calls when mode - github / github_issue
• MongoDBSaver missing indexes causes full collection scans - github / github_issue
• @langchain/[email protected] - github / github_release
• @langchain/[email protected] - github / github_release
• @langchain/[email protected] - github / github_release
• @langchain/[email protected] - github / github_release
• @langchain/[email protected] - github / github_release
• @langchain/[email protected] - github / github_release
• @langchain/[email protected] - github / github_release
• @langchain/[email protected] - github / github_release